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Evaluation of prognostic clinical and ECG parameters in patients after myocardial infarction by applying logistic regression method.

机译:应用Logistic回归方法评估心肌梗死后患者的预后临床和心电图参数。

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Noninvasive risk stratification of patients who have suffered myocardial infarction (MI) is one of the greatest challenges in today's cardiology. No single test has sufficient predictive ability. Therefore, a combination of the tests must be applied for better post-MI risk stratification. The purpose of this study was to assess noninvasive predictors of 2 years cardiac mortality in post-MI patients and create a stratification model for identification of high-risk patients. Clinical, electrocardiographic, and echocardiographic parameters were evaluated before hospital discharge in 180 survivors of acute MI (mean age 57.0 +/- 9.9, male 82.2%), followed up for 2 years. A multivariate logistic regression analysis was used to determine informative predictors of cardiac mortality. A clinical score was constructed using the regression coefficient from the multivariate model. During follow-up, 16 deaths (8.8%) occurred. Multivariate analysis identified a combination of six variables that showed the strongest association with cardiac mortality. Based on the coefficients of the logistic regression, six variables were used to create a scoring system: filtered QRS duration (QRSd) >114 ms, coefficient of variation (Cv) or=445 ms, left ventricular ejection fraction (LVEF)
机译:患有心肌梗塞(MI)的患者的非侵入性风险分层是当今心脏病学的最大挑战之一。没有任何一项测试具有足够的预测能力。因此,必须采用多种测试方法以更好地进行心梗后风险分层。这项研究的目的是评估心梗后患者2年心源性死亡的非侵入性预测因素,并建立分层模型以识别高危患者。在出院前对180例急性心肌梗死幸存者(平均年龄57.0 +/- 9.9,男性82.2%)进行了临床,心电图和超声心动图参数评估,随访了2年。多元logistic回归分析用于确定心脏死亡的资料性预测指标。使用来自多元模型的回归系数构建临床评分。在随访期间,发生了16例死亡(8.8%)。多变量分析确定了六个变量的组合,这些变量显示出与心脏死亡率最强的关联。根据逻辑回归系数,使用六个变量创建评分系统:过滤后的QRS持续时间(QRSd)> 114 ms,变异系数(Cv)<或= 2.5%,最大校正QT间隔(QTcmax)>或= 445毫秒,左心室射血分数(LVEF)<或= 30%,不使用β-受体阻滞剂(BB),以及不进行初次经皮冠状动脉介入治疗(PCI)进行治疗。分数等于或大于10的临界值定义了“高危”患者,其敏感性为75%,特异性为70%。低风险和高风险组的死亡率分别为12.5%和87.5%。接收机工作特性(ROC)分析得出曲线下面积为0.88。拟议的评分系统在预测MI后人群中2年心源性死亡风险方面可能很有价值,并且可以将患者分为低危和高危人群。

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